{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KSF3P7IxeF8xRlmXlM57nlWa7IAjK/SaO4wN9/rIsO/SZAfCJT/wy6zvCjJ++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\nG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V7ekgprF6kfa083sHtA7x+kLhpzhIxUZ4BNquKBO+8codmke1OrB/D4HrQaFk7YdyvJ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S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\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4jSQ/UZoGwrm9imIW92msderpi6DZbSFhd3M+jZEweeIpH2POWs4LAVtmKSqLKQtVp4lG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87+XKCPSysdVNiVvbK2jZVVcleMj3rY6tA4SmONV3/hKwCAV3/1VUDReAwrEXxFRtPH73yCOgtg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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b6d1c06dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837 ...,  126.64218367  125.86195918\n",
      "  114.39957143]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b681ddfda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.059231\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 23.771987 analytic: 23.771987, relative error: 1.160663e-11\n",
      "numerical: 6.826474 analytic: 6.826474, relative error: 4.140054e-11\n",
      "numerical: 3.100865 analytic: 3.100865, relative error: 2.004181e-11\n",
      "numerical: -3.253668 analytic: -3.253668, relative error: 1.549980e-10\n",
      "numerical: -6.540415 analytic: -6.540415, relative error: 4.280015e-11\n",
      "numerical: -14.306282 analytic: -14.306282, relative error: 4.437781e-11\n",
      "numerical: 28.550876 analytic: 28.550876, relative error: 7.371810e-12\n",
      "numerical: 11.075702 analytic: 11.075702, relative error: 7.348359e-12\n",
      "numerical: -9.369932 analytic: -9.369932, relative error: 2.143379e-11\n",
      "numerical: -10.543547 analytic: -10.543547, relative error: 3.052655e-12\n",
      "numerical: 17.029266 analytic: 17.029266, relative error: 1.155036e-11\n",
      "numerical: 32.928829 analytic: 32.928829, relative error: 4.709968e-12\n",
      "numerical: 14.818867 analytic: 14.818867, relative error: 1.899211e-11\n",
      "numerical: -6.882339 analytic: -6.832652, relative error: 3.622822e-03\n",
      "numerical: 3.315364 analytic: 3.315364, relative error: 1.427067e-10\n",
      "numerical: 5.744831 analytic: 5.744831, relative error: 1.273735e-11\n",
      "numerical: 34.001368 analytic: 34.001368, relative error: 3.713690e-12\n",
      "numerical: -0.189259 analytic: -0.189259, relative error: 8.655873e-10\n",
      "numerical: 1.246935 analytic: 1.246935, relative error: 2.726330e-10\n",
      "numerical: 6.915871 analytic: 6.915871, relative error: 1.410119e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "as hinge loss is not differentiable when you get close to the point where this non differentiability occurs your estimation of the gradient will be different depending on what direction you come from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.059231e+00 computed in 0.117613s\n",
      "Vectorized loss: 9.059231e+00 computed in 0.005506s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.115111s\n",
      "Vectorized loss and gradient: computed in 0.005004s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 5])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,7])\n",
    "y = np.arange(10)\n",
    "print(y)\n",
    "np.random.choice(x,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 795.272803\n",
      "iteration 100 / 1500: loss 290.061074\n",
      "iteration 200 / 1500: loss 108.921472\n",
      "iteration 300 / 1500: loss 42.938134\n",
      "iteration 400 / 1500: loss 19.016722\n",
      "iteration 500 / 1500: loss 9.769999\n",
      "iteration 600 / 1500: loss 6.825635\n",
      "iteration 700 / 1500: loss 6.001713\n",
      "iteration 800 / 1500: loss 5.639697\n",
      "iteration 900 / 1500: loss 5.311211\n",
      "iteration 1000 / 1500: loss 5.488161\n",
      "iteration 1100 / 1500: loss 5.300696\n",
      "iteration 1200 / 1500: loss 5.489209\n",
      "iteration 1300 / 1500: loss 6.082642\n",
      "iteration 1400 / 1500: loss 4.892580\n",
      "That took 8.054707s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kQZMkSfNnQSuS1voqTg2NlzqGJElahixoRXJFRz0v9J4rdQxJkrQMWdCK5Jr1Tew7OcR4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b680ee4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.370898\n",
      "validation accuracy: 0.383000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "1e-07\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "5.64444444444e-06\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.11888888889e-05\n",
      "1.67333333333e-05\n",
      "1.67333333333e-05\n",
      "1.67333333333e-05\n",
      "1.67333333333e-05\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-b1680d149259>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     42\u001b[0m                       num_iters=10, verbose=False)\n\u001b[1;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0my_train_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0mtrain_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[1;31m###########################################################################\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m     \u001b[0mpred_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred_scores\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "range_lr = np.linspace(learning_rates[0],learning_rates[1],10)\n",
    "range_reg = np.linspace(regularization_strengths[0],regularization_strengths[1],10)\n",
    "\n",
    "for cur_lr in range_lr: #go over the learning rates\n",
    "    for cur_reg in range_reg:#go over the regularization strength\n",
    "                \n",
    "        svm.train(X_train, y_train, learning_rate=cur_lr, reg=cur_reg,\n",
    "                      num_iters=10, verbose=False)\n",
    "        \n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        \n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        # FIX storing results\n",
    "        results = {(cur_lr,cur_reg):(train_acc,val_acc)}\n",
    "\n",
    "        print(results)\n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
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